Price Movement Forecasting Issue
Given a model that takes in a price and a second value, such as a moving average of the price, the following configuration is my recommendation.  This is based on training on a history of 45 input time steps and forecasting out 10 steps in the future.  I have assumed that your 'num_steps' is a stride through the training data.  Note that I have not tested this and you will most likely need to tweak a parameter or two:

{
    "data": {
        "sequence_length": 55,
        "train_test_split": 0.85,
        "normalise": true,
        "num_steps": 5
    },
    "training": {
        "epochs":200,
        "batch_size": 64
    },
    "model": {
        "loss": "mse",
        "optimizer": "adam",
        "layers": [
            {
                "type": "lstm",
                "neurons": 100,
                "input_timesteps": 45,
                "input_dim": 2,
                "return_seq": true,
                "activation": "relu"
            },
            {
                "type": "dropout",
                "rate": 0.2
            },
            {
                "type": "lstm",
                "neurons": 100,
                "activation": "relu",
                "return_seq": false
            },
            {
                "type": "dropout",
                "rate": 0.2
            },
            {
                "type": "dense",
                "neurons": 1,
                "activation": "linear"
            }
        ]
    }
}


Try this out and plot your model's loss and accuracy by epoch.  See Display Deep Learning Model Training History in Keras for some information and code in this area.  Let me know if you are not using Keras and I will point you to another article/code solution.